{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "85f8e12c-1698-4cfc-8852-76efcef1b8e0",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-07-27T13:49:59.519429Z",
     "start_time": "2024-07-27T13:49:59.514470Z"
    },
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "execution": {
     "iopub.execute_input": "2024-07-29T14:55:13.075112Z",
     "iopub.status.busy": "2024-07-29T14:55:13.074925Z",
     "iopub.status.idle": "2024-07-29T14:55:13.078314Z",
     "shell.execute_reply": "2024-07-29T14:55:13.077861Z",
     "shell.execute_reply.started": "2024-07-29T14:55:13.075096Z"
    },
    "id": "85f8e12c-1698-4cfc-8852-76efcef1b8e0",
    "outputId": "5387a039-7ae6-456d-959d-92a3166cf67a"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "env: API_KEY=替换为自己的\n",
      "env: BASE_URL=https://api.deepbricks.ai/v1\n"
     ]
    }
   ],
   "source": [
    "%env API_KEY=替换为自己的\n",
    "%env BASE_URL=https://api.deepbricks.ai/v1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "af375836-b870-458b-87d1-4e00565977eb",
   "metadata": {
    "editable": true,
    "execution": {
     "iopub.execute_input": "2024-07-11T15:36:31.385695Z",
     "iopub.status.busy": "2024-07-11T15:36:31.385368Z",
     "iopub.status.idle": "2024-07-11T15:36:31.389515Z",
     "shell.execute_reply": "2024-07-11T15:36:31.388754Z",
     "shell.execute_reply.started": "2024-07-11T15:36:31.385660Z"
    },
    "id": "af375836-b870-458b-87d1-4e00565977eb",
    "tags": []
   },
   "outputs": [],
   "source": [
    "%%capture --no-stderr\n",
    "!pip install -U langchain langchain_community pypdf sentence_transformers chromadb trulens_eval langchain_openai"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "e-Oy-FOJgB_A",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "execution": {
     "iopub.execute_input": "2024-07-29T14:25:14.021235Z",
     "iopub.status.busy": "2024-07-29T14:25:14.021060Z",
     "iopub.status.idle": "2024-07-29T14:25:20.011316Z",
     "shell.execute_reply": "2024-07-29T14:25:20.010954Z",
     "shell.execute_reply.started": "2024-07-29T14:25:14.021221Z"
    },
    "id": "e-Oy-FOJgB_A",
    "outputId": "7164ffc8-48fd-419b-bb78-a3049e9749b8"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2024-07-29 22:25:17.955344: I tensorflow/tsl/cuda/cudart_stub.cc:28] Could not find cuda drivers on your machine, GPU will not be used.\n",
      "2024-07-29 22:25:17.984357: I tensorflow/core/platform/cpu_feature_guard.cc:182] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.\n",
      "To enable the following instructions: AVX2 FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.\n",
      "2024-07-29 22:25:18.582597: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "langchain                     0.2.7\n",
      "langchain_community           0.2.7\n",
      "langchain_openai              \n",
      "pypdf                         4.2.0\n",
      "sentence_transformers         2.7.0\n",
      "chromadb                      0.5.3\n",
      "trulens_eval                  0.33.0\n"
     ]
    }
   ],
   "source": [
    "import langchain, langchain_community, pypdf, sentence_transformers, chromadb, trulens_eval, langchain_openai\n",
    "\n",
    "for module in (langchain, langchain_community, langchain_openai, pypdf, sentence_transformers, chromadb, trulens_eval):\n",
    "    print(f\"{module.__name__:<30}{module.__version__ if hasattr(module, '__version__') else ''}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "yL9d4F1t_KuL",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "execution": {
     "iopub.execute_input": "2024-07-29T14:25:20.012527Z",
     "iopub.status.busy": "2024-07-29T14:25:20.012166Z",
     "iopub.status.idle": "2024-07-29T14:25:20.768335Z",
     "shell.execute_reply": "2024-07-29T14:25:20.765419Z",
     "shell.execute_reply.started": "2024-07-29T14:25:20.012513Z"
    },
    "id": "yL9d4F1t_KuL",
    "outputId": "81e9450d-6dc2-40c9-b8da-e8a6f253d8e2"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "langchain-openai                                  0.1.7\n"
     ]
    }
   ],
   "source": [
    "!pip list |grep -i langchain-openai"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "1e2c72b8-ee12-4130-af88-699998aa230c",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-07-29T14:25:20.772535Z",
     "iopub.status.busy": "2024-07-29T14:25:20.771744Z",
     "iopub.status.idle": "2024-07-29T14:25:20.782636Z",
     "shell.execute_reply": "2024-07-29T14:25:20.780291Z",
     "shell.execute_reply.started": "2024-07-29T14:25:20.772448Z"
    },
    "id": "1e2c72b8-ee12-4130-af88-699998aa230c"
   },
   "outputs": [],
   "source": [
    "import os\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "841d2b02-ad06-40d2-b11f-c7adccec6ca2",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-07-29T14:26:05.439424Z",
     "iopub.status.busy": "2024-07-29T14:26:05.438650Z",
     "iopub.status.idle": "2024-07-29T14:26:05.449482Z",
     "shell.execute_reply": "2024-07-29T14:26:05.447565Z",
     "shell.execute_reply.started": "2024-07-29T14:26:05.439350Z"
    },
    "id": "841d2b02-ad06-40d2-b11f-c7adccec6ca2"
   },
   "outputs": [],
   "source": [
    "# 如果已经下载到本地，可以替换为本地路径\n",
    "EMBEDDING_MODEL_PATH = 'BAAI/bge-large-zh-v1.5'\n",
    "dt = '20240713'\n",
    "version = 'v1'\n",
    "\n",
    "output_dir = os.path.join(os.path.pardir, 'outputs', f'{version}_{dt}')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "32092b6f-d585-4c84-8521-234b26eb58a7",
   "metadata": {
    "id": "32092b6f-d585-4c84-8521-234b26eb58a7"
   },
   "source": [
    "加载问答对"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "5dca0705-236a-4afc-a0df-58ce953d4e4f",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-07-29T14:25:20.793509Z",
     "iopub.status.busy": "2024-07-29T14:25:20.793379Z",
     "iopub.status.idle": "2024-07-29T14:25:20.900371Z",
     "shell.execute_reply": "2024-07-29T14:25:20.899883Z",
     "shell.execute_reply.started": "2024-07-29T14:25:20.793497Z"
    },
    "id": "5dca0705-236a-4afc-a0df-58ce953d4e4f"
   },
   "outputs": [],
   "source": [
    "qa_df = pd.read_excel(os.path.join(output_dir, 'question_answer.xlsx'))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cf7e81e3-4c82-4842-aef5-7592caaf1d39",
   "metadata": {
    "id": "cf7e81e3-4c82-4842-aef5-7592caaf1d39"
   },
   "source": [
    "# 文档处理"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "da15f02e-3131-43fb-81c5-f4da615c449b",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-07-29T14:25:20.901124Z",
     "iopub.status.busy": "2024-07-29T14:25:20.900851Z",
     "iopub.status.idle": "2024-07-29T14:25:21.971732Z",
     "shell.execute_reply": "2024-07-29T14:25:21.971345Z",
     "shell.execute_reply.started": "2024-07-29T14:25:20.901109Z"
    },
    "id": "da15f02e-3131-43fb-81c5-f4da615c449b"
   },
   "outputs": [],
   "source": [
    "from langchain_community.document_loaders import PyPDFLoader\n",
    "\n",
    "loader = PyPDFLoader(os.path.join(os.path.pardir, 'data', '2024全球经济金融展望报告.pdf'))\n",
    "documents = loader.load()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "74fe856a-7c19-4c3c-bb30-7abfa6298f74",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-07-29T14:25:21.972345Z",
     "iopub.status.busy": "2024-07-29T14:25:21.972216Z",
     "iopub.status.idle": "2024-07-29T14:25:21.977001Z",
     "shell.execute_reply": "2024-07-29T14:25:21.976597Z",
     "shell.execute_reply.started": "2024-07-29T14:25:21.972331Z"
    },
    "id": "74fe856a-7c19-4c3c-bb30-7abfa6298f74"
   },
   "outputs": [],
   "source": [
    "from uuid import uuid4\n",
    "import os\n",
    "import pickle\n",
    "\n",
    "from langchain.text_splitter import RecursiveCharacterTextSplitter\n",
    "from langchain_community.vectorstores import Chroma\n",
    "\n",
    "def split_docs(documents, filepath, chunk_size=400, chunk_overlap=40, seperators=['\\n\\n\\n', '\\n\\n'], force_split=False):\n",
    "    if os.path.exists(filepath) and not force_split:\n",
    "        print('found cache, restoring...')\n",
    "        return pickle.load(open(filepath, 'rb'))\n",
    "\n",
    "    splitter = RecursiveCharacterTextSplitter(\n",
    "        chunk_size=chunk_size,\n",
    "        chunk_overlap=chunk_overlap,\n",
    "        separators=seperators\n",
    "    )\n",
    "    split_docs = splitter.split_documents(documents)\n",
    "    for chunk in split_docs:\n",
    "        chunk.metadata['uuid'] = str(uuid4())\n",
    "\n",
    "    pickle.dump(split_docs, open(filepath, 'wb'))\n",
    "\n",
    "    return split_docs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "fh0N6CwXe-Ho",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "execution": {
     "iopub.execute_input": "2024-07-29T14:25:21.977549Z",
     "iopub.status.busy": "2024-07-29T14:25:21.977427Z",
     "iopub.status.idle": "2024-07-29T14:25:21.980810Z",
     "shell.execute_reply": "2024-07-29T14:25:21.980452Z",
     "shell.execute_reply.started": "2024-07-29T14:25:21.977536Z"
    },
    "id": "fh0N6CwXe-Ho",
    "outputId": "a87c07f0-70c5-4490-f655-b1a7c1317b46"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "found cache, restoring...\n"
     ]
    }
   ],
   "source": [
    "splitted_docs = split_docs(documents, os.path.join(output_dir, 'split_docs.pkl'), chunk_size=500, chunk_overlap=50)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "220dbc3a-fceb-4e49-a3f1-01e16660b2a6",
   "metadata": {
    "id": "220dbc3a-fceb-4e49-a3f1-01e16660b2a6"
   },
   "source": [
    "向量化"
   ]
  },
  {
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     "iopub.status.idle": "2024-07-29T14:26:08.147948Z",
     "shell.execute_reply": "2024-07-29T14:26:08.147440Z",
     "shell.execute_reply.started": "2024-07-29T14:26:07.516586Z"
    },
    "id": "f7124830-dba9-4609-aedb-f81f4e388fa4",
    "outputId": "c02cf8b7-6266-492d-c026-a930e451292c"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "device: cuda\n"
     ]
    }
   ],
   "source": [
    "from langchain.embeddings import HuggingFaceBgeEmbeddings\n",
    "import torch\n",
    "\n",
    "device = 'cuda' if torch.cuda.is_available() else 'cpu'\n",
    "print(f'device: {device}')\n",
    "\n",
    "embeddings = HuggingFaceBgeEmbeddings(\n",
    "    model_name=EMBEDDING_MODEL_PATH,\n",
    "    model_kwargs={'device': device},\n",
    "    encode_kwargs={'normalize_embeddings': True}\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "663ef1a4-5866-4f6b-8d9d-4724f62142cb",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-07-29T14:26:08.428800Z",
     "iopub.status.busy": "2024-07-29T14:26:08.428008Z",
     "iopub.status.idle": "2024-07-29T14:26:08.441765Z",
     "shell.execute_reply": "2024-07-29T14:26:08.439550Z",
     "shell.execute_reply.started": "2024-07-29T14:26:08.428727Z"
    },
    "id": "663ef1a4-5866-4f6b-8d9d-4724f62142cb"
   },
   "outputs": [],
   "source": [
    "from tqdm.auto import tqdm\n",
    "\n",
    "def get_vector_db(docs, store_path, force_rebuild=False):\n",
    "    if not os.path.exists(store_path):\n",
    "        force_rebuild = True\n",
    "\n",
    "    if force_rebuild:\n",
    "        vector_db = Chroma.from_documents(\n",
    "            docs,\n",
    "            embedding=embeddings,\n",
    "            persist_directory=store_path\n",
    "        )\n",
    "    else:\n",
    "        vector_db = Chroma(\n",
    "            persist_directory=store_path,\n",
    "            embedding_function=embeddings\n",
    "        )\n",
    "    return vector_db"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "41821b04-afc4-4a9b-98d2-f95f7bf83f74",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-07-29T14:26:08.693939Z",
     "iopub.status.busy": "2024-07-29T14:26:08.693172Z",
     "iopub.status.idle": "2024-07-29T14:26:09.212985Z",
     "shell.execute_reply": "2024-07-29T14:26:09.212521Z",
     "shell.execute_reply.started": "2024-07-29T14:26:08.693866Z"
    },
    "id": "41821b04-afc4-4a9b-98d2-f95f7bf83f74"
   },
   "outputs": [],
   "source": [
    "vector_db = get_vector_db(splitted_docs, store_path=os.path.join(os.path.pardir, output_dir, 'chromadb', 'bge_large_v1.5'))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e84a6970-e740-4fe5-ab76-1f43b2f5da6b",
   "metadata": {
    "id": "e84a6970-e740-4fe5-ab76-1f43b2f5da6b"
   },
   "source": [
    "# 问答全流程"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dfff319f-9ca2-4a54-baee-53a3a15d6b8c",
   "metadata": {},
   "source": [
    "## 构建QA Chain"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "jl36b7QsQA0t",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-07-29T14:26:10.832506Z",
     "iopub.status.busy": "2024-07-29T14:26:10.831720Z",
     "iopub.status.idle": "2024-07-29T14:26:10.846116Z",
     "shell.execute_reply": "2024-07-29T14:26:10.845663Z",
     "shell.execute_reply.started": "2024-07-29T14:26:10.832432Z"
    },
    "id": "jl36b7QsQA0t"
   },
   "outputs": [],
   "source": [
    "from langchain.llms import Ollama\n",
    "from langchain_core.output_parsers import StrOutputParser\n",
    "from langchain_core.runnables import RunnablePassthrough\n",
    "from langchain_core.prompts import PromptTemplate\n",
    "\n",
    "def format_docs(docs):\n",
    "    return \"\\n\\n\".join(doc.page_content for doc in docs)\n",
    "\n",
    "llm = Ollama(\n",
    "    model='qwen2:7b-instruct',\n",
    "    base_url=\"http://localhost:11434\"\n",
    ")\n",
    "\n",
    "prompt_tmpl = \"\"\"\n",
    "你是一个金融分析师，擅长根据所获取的信息片段，对问题进行分析和推理。\n",
    "你的任务是根据所获取的信息片段（<<<<context>>><<<</context>>>之间的内容）回答问题。\n",
    "回答保持简洁，不必重复问题，不要要添加描述性解释和与答案无关的任何内容。\n",
    "已知信息：\n",
    "<<<<context>>>\n",
    "{context}\n",
    "<<<</context>>>\n",
    "\n",
    "问题：{question}\n",
    "请回答：\n",
    "\"\"\"\n",
    "prompt = PromptTemplate.from_template(prompt_tmpl)\n",
    "retriever = vector_db.as_retriever(search_kwargs={'k': 4})\n",
    "\n",
    "rag_chain = (\n",
    "    {\"context\": retriever | format_docs, \"question\": RunnablePassthrough()}\n",
    "    | prompt\n",
    "    | llm\n",
    "    | StrOutputParser()\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "5146bd08-c194-4103-9141-8822d7e23734",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "execution": {
     "iopub.execute_input": "2024-07-29T14:26:11.056056Z",
     "iopub.status.busy": "2024-07-29T14:26:11.055709Z",
     "iopub.status.idle": "2024-07-29T14:26:15.329271Z",
     "shell.execute_reply": "2024-07-29T14:26:15.328759Z",
     "shell.execute_reply.started": "2024-07-29T14:26:11.056025Z"
    },
    "id": "5146bd08-c194-4103-9141-8822d7e23734",
    "outputId": "28f4edbd-5a9c-41fb-d634-ec024be4f4b0"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2023年10月美国ISM制造业PMI指数较上个月大幅下降了2.3个百分点。\n"
     ]
    }
   ],
   "source": [
    "print(rag_chain.invoke('2023年10月美国ISM制造业PMI指数较上月有何变化？'))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3cb01ef4-45d7-4236-9077-1ed66ad206a2",
   "metadata": {
    "id": "3cb01ef4-45d7-4236-9077-1ed66ad206a2"
   },
   "source": [
    "## 准备测试集"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "5c121686-1154-4092-b64a-ec10bc6c2391",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-07-29T14:34:59.144073Z",
     "iopub.status.busy": "2024-07-29T14:34:59.143299Z",
     "iopub.status.idle": "2024-07-29T14:34:59.149716Z",
     "shell.execute_reply": "2024-07-29T14:34:59.149386Z",
     "shell.execute_reply.started": "2024-07-29T14:34:59.143999Z"
    }
   },
   "outputs": [],
   "source": [
    "prediction_df = qa_df[qa_df['dataset'] == 'test'][['uuid', 'question', 'qa_type', 'answer']].rename(columns={'answer': 'ref_answer'})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "79de293f-35a2-4582-a035-dac777a22b89",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-07-29T14:27:42.841468Z",
     "iopub.status.busy": "2024-07-29T14:27:42.841255Z",
     "iopub.status.idle": "2024-07-29T14:33:07.300259Z",
     "shell.execute_reply": "2024-07-29T14:33:07.299830Z",
     "shell.execute_reply.started": "2024-07-29T14:27:42.841454Z"
    }
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "c13a1772be6c4d7eaef7ffa59adb707d",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/100 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "answer_dict = {}\n",
    "\n",
    "for idx, row in tqdm(prediction_df.iterrows(), total=len(prediction_df)):\n",
    "    uuid = row['uuid']\n",
    "    question = row['question']\n",
    "    answer = rag_chain.invoke(question)\n",
    "    answer_dict[question] = {\n",
    "        'uuid': uuid,\n",
    "        'ref_answer': row['answer'],\n",
    "        'gen_answer': answer\n",
    "    }"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "479cc959-841b-49e0-b189-264d9e108fab",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-07-29T14:35:06.935781Z",
     "iopub.status.busy": "2024-07-29T14:35:06.935593Z",
     "iopub.status.idle": "2024-07-29T14:35:06.939108Z",
     "shell.execute_reply": "2024-07-29T14:35:06.938675Z",
     "shell.execute_reply.started": "2024-07-29T14:35:06.935768Z"
    }
   },
   "outputs": [],
   "source": [
    "prediction_df.loc[:, 'gen_answer'] = prediction_df['question'].apply(lambda q: answer_dict[q]['gen_answer'])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5c2bfd4c-0d69-4f2c-8650-150e0be5ef69",
   "metadata": {
    "id": "5c2bfd4c-0d69-4f2c-8650-150e0be5ef69"
   },
   "source": [
    "# 评估"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "c9a8429a-df66-4ddc-9d11-689fbd7a7655",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-07-29T14:35:08.160491Z",
     "iopub.status.busy": "2024-07-29T14:35:08.159718Z",
     "iopub.status.idle": "2024-07-29T14:35:08.168350Z",
     "shell.execute_reply": "2024-07-29T14:35:08.168042Z",
     "shell.execute_reply.started": "2024-07-29T14:35:08.160418Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>uuid</th>\n",
       "      <th>question</th>\n",
       "      <th>qa_type</th>\n",
       "      <th>ref_answer</th>\n",
       "      <th>gen_answer</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>e73a0c9d-d42b-4350-a4c3-b38bf67c68a5</td>\n",
       "      <td>报告的发布机构是什么？</td>\n",
       "      <td>detailed</td>\n",
       "      <td>中国银行研究院</td>\n",
       "      <td>报告的发布机构是中国银行研究院。</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>e73a0c9d-d42b-4350-a4c3-b38bf67c68a5</td>\n",
       "      <td>报告的发布日期是什么时候？</td>\n",
       "      <td>detailed</td>\n",
       "      <td>2023年12月12日</td>\n",
       "      <td>报告的发布日期是2023年12月12日。</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>e73a0c9d-d42b-4350-a4c3-b38bf67c68a5</td>\n",
       "      <td>2023年全球经济增长有什么特点？</td>\n",
       "      <td>detailed</td>\n",
       "      <td>全球经济增长动力持续回落，各国复苏分化，发达经济体增速放缓，新兴经济体表现稳定。</td>\n",
       "      <td>2023年全球经济增长呈现“复苏+分化”的特点。发达经济体增速明显放缓，其中欧元区和英国经济...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>e73a0c9d-d42b-4350-a4c3-b38bf67c68a5</td>\n",
       "      <td>全球贸易增长情况如何？</td>\n",
       "      <td>detailed</td>\n",
       "      <td>全球贸易增长乏力。</td>\n",
       "      <td>根据所给信息片段，全球货物贸易量和价格指数均承压下行。在2023年1-8月期间，全球货物贸易...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>e73a0c9d-d42b-4350-a4c3-b38bf67c68a5</td>\n",
       "      <td>展望2024年，全球经济复苏的预期如何？</td>\n",
       "      <td>detailed</td>\n",
       "      <td>全球经济复苏预计将依旧疲软。</td>\n",
       "      <td>预计2024年全球经济复苏将保持疲软态势，并可能加剧分化。发达经济体面临“遏通胀和稳增长”的...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                   uuid              question   qa_type  \\\n",
       "0  e73a0c9d-d42b-4350-a4c3-b38bf67c68a5           报告的发布机构是什么？  detailed   \n",
       "1  e73a0c9d-d42b-4350-a4c3-b38bf67c68a5         报告的发布日期是什么时候？  detailed   \n",
       "2  e73a0c9d-d42b-4350-a4c3-b38bf67c68a5     2023年全球经济增长有什么特点？  detailed   \n",
       "3  e73a0c9d-d42b-4350-a4c3-b38bf67c68a5           全球贸易增长情况如何？  detailed   \n",
       "5  e73a0c9d-d42b-4350-a4c3-b38bf67c68a5  展望2024年，全球经济复苏的预期如何？  detailed   \n",
       "\n",
       "                                 ref_answer  \\\n",
       "0                                   中国银行研究院   \n",
       "1                               2023年12月12日   \n",
       "2  全球经济增长动力持续回落，各国复苏分化，发达经济体增速放缓，新兴经济体表现稳定。   \n",
       "3                                 全球贸易增长乏力。   \n",
       "5                            全球经济复苏预计将依旧疲软。   \n",
       "\n",
       "                                          gen_answer  \n",
       "0                                   报告的发布机构是中国银行研究院。  \n",
       "1                               报告的发布日期是2023年12月12日。  \n",
       "2  2023年全球经济增长呈现“复苏+分化”的特点。发达经济体增速明显放缓，其中欧元区和英国经济...  \n",
       "3  根据所给信息片段，全球货物贸易量和价格指数均承压下行。在2023年1-8月期间，全球货物贸易...  \n",
       "5  预计2024年全球经济复苏将保持疲软态势，并可能加剧分化。发达经济体面临“遏通胀和稳增长”的...  "
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "prediction_df.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "d2c24729-a716-4468-a6b3-34960840469d",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-07-29T14:35:16.478172Z",
     "iopub.status.busy": "2024-07-29T14:35:16.477394Z",
     "iopub.status.idle": "2024-07-29T14:35:16.491693Z",
     "shell.execute_reply": "2024-07-29T14:35:16.491257Z",
     "shell.execute_reply.started": "2024-07-29T14:35:16.478099Z"
    },
    "id": "d2c24729-a716-4468-a6b3-34960840469d"
   },
   "outputs": [],
   "source": [
    "from langchain_openai import ChatOpenAI\n",
    "\n",
    "judge_llm = ChatOpenAI(\n",
    "    api_key=os.environ['API_KEY'],\n",
    "    base_url=os.environ['BASE_URL'],\n",
    "    # model_name='qwen2-72b-instruct'\n",
    "    # model_name='qwen-long' 0.68\n",
    "    model_name='gpt-4o-mini'\n",
    ")\n",
    "\n",
    "def evaluate(question, ref_answer, gen_answer):\n",
    "    \"\"\"\n",
    "    对预测结果进行打分\n",
    "    :param question: 问题\n",
    "    :param ref_answer: 参考答案\n",
    "    :param gen_answer: 生成的答案\n",
    "    :return 打分模型原始返回结果\n",
    "    \"\"\"\n",
    "    prompt = \"\"\"\n",
    "你是一个经济学博士，现在我有一系列问题，有一个助手已经对这些问题进行了回答，你需要参照参考答案，评价这个助手的回答是否正确，仅回复“是”或“否”即可，不要带其他描述性内容或无关信息。\n",
    "问题：\n",
    "<question>\n",
    "{{question}}\n",
    "</question>\n",
    "\n",
    "参考答案：\n",
    "<ref_answer>\n",
    "{{ref_answer}}\n",
    "</ref_answer>\n",
    "\n",
    "助手回答：\n",
    "<gen_answer>\n",
    "{{gen_answer}}\n",
    "</gen_answer>\n",
    "请评价：\n",
    "    \"\"\".replace('{{question}}', question).replace('{{ref_answer}}', str(ref_answer)).replace('{{gen_answer}}', gen_answer).strip()\n",
    "    result = judge_llm.invoke(prompt).content\n",
    "    return result"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "zL_cHwuY_Sop",
   "metadata": {
    "id": "zL_cHwuY_Sop"
   },
   "source": [
    "或者也可以使用Ollama提供的模型"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "f13a7be3-435f-44ae-92fc-0cca5d1db031",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-07-29T14:26:34.667491Z",
     "iopub.status.busy": "2024-07-29T14:26:34.666708Z",
     "iopub.status.idle": "2024-07-29T14:26:34.676011Z",
     "shell.execute_reply": "2024-07-29T14:26:34.673717Z",
     "shell.execute_reply.started": "2024-07-29T14:26:34.667418Z"
    },
    "id": "f13a7be3-435f-44ae-92fc-0cca5d1db031"
   },
   "outputs": [],
   "source": [
    "# from langchain.llms import Ollama\n",
    "\n",
    "# judge_llm = Ollama(\n",
    "#     model='qwen2:72b-instruct',\n",
    "#     base_url=\"http://localhost:11434\"\n",
    "# )"
   ]
  },
  {
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      "/tmp/ipykernel_2557091/3333121548.py:1: FutureWarning: Downcasting behavior in `replace` is deprecated and will be removed in a future version. To retain the old behavior, explicitly call `result.infer_objects(copy=False)`. To opt-in to the future behavior, set `pd.set_option('future.no_silent_downcasting', True)`\n",
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